Prevention of Occupational Diseases in Small and Medium-Sized Manufacturing Enterprises in Quebec (CANADA)-Portrayal of Elements Influencing OHS Performance
Bibliographic record
Abstract
Unlike workplace accidents, occupational diseases are often underestimated and underreported since their effects appear gradually over time. They are even on the increase in the province of Quebec (Canada), especially in small and medium-sized enterprises (SMEs), where they are less likely to receive medical attention. The aim of this four-stage study is therefore to describe how prevention of occupational disease is practiced in this type of business and identify a way forward to improve the protection of worker health and well-being in Quebec. The present article focuses on the first two stages, namely reviewing the literature to catalog the elements of prevention and identifying the most relevant elements. Stages 3 and 4, in which gathered field data on the application of these elements and analyzed their relative effectiveness using descriptive statistics, are reported in Part 2 [1]. Despite the limitations of this research method, we portray in detail the elements that appear to have the most influence on occupational disease prevention in small to medium-sized manufacturing enterprises, and thus identify the strengths and weaknesses of occupational health and safety performance in this setting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".